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demo.py
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demo.py
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'''
@author Munawar Hasan <[email protected]>
'''
from qml import Model
import pandas as pd
import numpy as np
data_dir = "data/"
tr_file = data_dir +"tr_probabilities.csv"
te_file = data_dir +"te_probabilities.csv"
unitary_matrix = [
[
-0.33500492 - 0.71478153j, 0.32202088 - 0.31921061j,
-0.19005727 - 0.26556473j, 0.25141268 - 0.03756969j
],
[
0.06510655 - 0.12600615j, 0.06592668 - 0.61643856j,
0.35661372 + 0.21461067j, -0.60806694 + 0.229269j
],
[
0.36498115 - 0.44204005j, -0.56451608 - 0.01413113j,
-0.47660056 + 0.27633166j, -0.15701817 - 0.15604254j
],
[
-0.0325527 - 0.16453671j, -0.29302758 + 0.07294554j,
0.51825632 - 0.384741j, -0.15690396 - 0.6629085j
]
]
def test_qml(csv_filename):
df = pd.read_csv(csv_filename)
print(df.head())
model_1_proba = df['model_1_proba'].tolist()
model_2_proba = df['model_2_proba'].tolist()
labels = df['labels'].tolist()
labels = list(map(int, labels))
print(model_1_proba[0], model_2_proba[0], labels[0])
X = np.zeros([len(model_1_proba), 2], dtype='float32')
for index in range(len(model_1_proba)):
X[index, :] = [model_1_proba[index], model_2_proba[index]]
print("Dataset Shape: ", X.shape)
model = Model(num_of_qubits=2, U=unitary_matrix)
y_pred = model.compute(x=X, pred=True, pred_index=None)
dd = model.get_metrics(y_true=labels, y_pred=y_pred)
return dd
def test_stub1():
print("<<evaluating qml on train dataset>> ....")
train_metrics = test_qml(tr_file)
print("train metrics: ", train_metrics)
print("<<evaluating qml on test dataset>> ....")
test_metrics = test_qml(te_file)
print("test metrics: ", test_metrics)
if __name__ == '__main__':
print("qml demo .....")
test_stub1()